You're reading from Building Neo4j-Powered Applications with LLMs Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI

Product type Paperback

Published in Jun 2025

Publisher Packt

ISBN-13 9781836206231

Length 312 pages

Edition 1st Edition

Languages

Cypher

Tools

Cypher Query

Concepts

GPT/LLMs

Authors (2):

Ravindranatha Anthapu

Siddhant Agarwal

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Table of Contents (20) Chapters

Preface

1. Part: 1 Introducing RAG and Knowledge Graphs for LLM Grounding

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2. Introducing LLMs, RAGs, and Neo4j Knowledge Graphs FREE CHAPTER

3. Demystifying RAG

4. Building a Foundational Understanding of Knowledge Graph for Intelligent Applications

5. Part 2: Integrating Haystack with Neo4j: A Practical Guide to Building AI-Powered Search

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6. Building Your Neo4j Graph with Movies Dataset

7. Implementing Powerful Search Functionalities with Neo4j and Haystack

8. Exploring Advanced Knowledge Graph Capabilities with Neo4j

9. Part 3: Building an Intelligent Recommendation System with Neo4j, Spring AI, and LangChain4j

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10. Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems

11. Constructing a Recommendation Graph with H&M Personalization Dataset

12. Integrating LangChain4j and Spring AI with Neo4j

13. Creating an Intelligent Recommendation System

14. Part 4: Deploying Your GenAI Application in the Cloud

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15. Choosing the Right Cloud Platform for GenAI Applications

16. Deploying Your Application on the Google Cloud

17. Epilogue

18. Other Books You May Enjoy

19. Index

Setting up a Google Cloud project and services

Google Cloud provides a robust, developer-friendly platform for deploying modern applications, including GenAI-powered solutions. With tools such as Cloud Run, Artifact Registry, and Cloud Build, Google Cloud enables you to go from code to scalable, serverless deployment with minimal operational overhead.

Although your Haystack chatbot uses OpenAI for language processing, Google Cloud plays a critical role in hosting the application, managing container builds, and securely storing your Docker images. In this section, you will configure your Google Cloud project, enable only the necessary services (such as Cloud Run, Cloud Build, and Artifact Registry), and prepare your environment for deployment.

By the end of this section, your project will be cloud-ready, with all the services and permissions in place to deploy your chatbot using Google Cloud’s serverless infrastructure.

Let us get started by setting up your project...